`
Introduction
Obj
ti
`Objective
`Methods
`Results
`Conclusion
2Malignant pleural mesothelioma (MPM) is the most common form of mesothelioma
` a rare cancer associated with long latency period (i.e. 20 to 40 years), late diagnosis, poor prognosis, and expected survival of 4 to 12 months without treatment
` caused by asbestos exposure in up to 80% of cases ` 3000 new cases and deaths each year in United States ` no recognized standard of care for second-line treatment
(treatment given after initial chemotherapy for advanced (treatment given after initial chemotherapy for advanced-stage disease)
` Past studies have analyzed longitudinal data and time-to-event (survival) data separately
to event (survival) data separately
` Methods to demonstrate the association between patient-reported outcomes (PROs) and survival ( ) endpoints in oncology are needed
` Use of latent growth curve models (LGCM) permits
t f h i PRO b th ithi d b t
assessment of change in PROs both within and between individuals
` The estimated latent variables can then be incorporated ` The estimated latent variables can then be incorporated
into survival models and analyzed as predictors of survival
` To estimate the pattern of patient-reported outcome
(PRO) change and its association with progression free (PRO) change and its association with progression-free survival (PFS) to better demonstrate treatment benefit
Nine questions: Are patient self-reported appetite, fatigue, cough, dyspnea, hemoptysis, pain, symptom distress,
cough, dyspnea, hemoptysis, pain, symptom distress, interference with activity level, and quality of life (QoL) associated with PFS in patients undergoing second-line treatment for advanced MPM?
` HAA: The PROs are associated with PFSe Os a e assoc ated t S
` Ho: The PROs are not associated with PFS
` Post-hoc analysis of secondary PRO and PFS data
collected from a phase III randomized controlled trial for
d li t t t f MPM (J t l 2008)
second-line treatment of MPM (Jassem et al., 2008) ` Sample consisted of 243 patients randomized to two
treatment groups treatment groups
o Best Supportive Care (BSC) – 120
o Pemetrexed + BSC – 123
` PROs measured ~ every 3 weeks using the Lung Cancer Symptom Scale (LCSS) questionnaire
1. Latent growth curve models (LGCMs) constructed for each of the nine PRO (LCSS) items with and without fi d t t t ff t
fixed treatment effect
2. Survival models for PFS with treatment effect
J i d l f PRO d PFS d i (M hé
3. Joint models of PRO and PFS endpoints (Muthén et al., 2009)
Demographic characteristics: o age (mean - 60 years)g ( y )
o gender (77.8% male)
o race (89.7% White)
Baseline disease characteristics: Baseline disease characteristics:
o performance status (median score – 90)
o stage of disease (60.1% Stage IV)
classification of histological diagnosis (72 4% epithelioid subtype)
o classification of histological diagnosis (72.4% epithelioid subtype)
o response to prior chemotherapy (41.6% stable disease)
Two-sided t test:
10
Survival curves for Kaplan-Meier versus Cox proportional hazard (PH) model
Median PFS (months) BSC = 1.5 P+BSC = 3.6 Log rank P = .0148
Survival curves for Kaplan-Meier versus nonproportional hazard model
The non-PH model curves now representative of the KM curves
PRO PFS regressed on PFS regressed on regressed on intercept regressed on slope appetite loss p = 0.000 p = 0.003 appetite loss cough dyspnea symptom distress p 0.000 p = 0.010 p = 0.002 p = 0 043 p 0.003 p = 0.053 p = 0.008 p = 0 005 symptom distress
interference with activity level
p 0.043 p = 0.011 p 0.005 p = 0.000 pain p = 0.000 p = 0.207 QoL p = 0.239 p = 0.029 fatigue p = 0.285 p = 0.212
* the joint hemoptysis model did not converge
` We jointly estimated a shared parameter (random effects) model using a non-proportional hazards equation and a latent growth curve equation to
respectively represent the time-to-event clinical endpoint (PFS) and the repeatedly measured PROs
` Our conceptual model that shared growth factors (a ` Our conceptual model that shared growth factors (a
random intercept and a random slope) resulted in the observed outcomes was supported by the data
` The associated patient-reported outcomes support the notion that self-reported symptoms correspond to
observed clinical outcomes
` Patient-reported outcome (PRO): a measurement of any
aspect of a patient’s health status that comes directly from the aspect of a patient s health status that comes directly from the patient (i.e., without the interpretation of the patient’s
responses by a physician or anyone else), and includes multidimensional data such as functional and health status multidimensional data such as functional and health status, well being, symptoms, quality of life (QoL), and satisfaction with treatment (Berger et al., 2003, p. 175; FDA, 2009)
` Progression-free survival (PFS): the time from randomization
in a clinical trial to the date of tumor (disease) progression or death from any cause, whereby tumor (disease) progression is clearly defined in the research protocol (FDA, 2007)
` Multivariate processes of the repeated PRO measurements to
confirm whether symptom worsening occurred prior to disease i
progression
` Sensitivity analyses to account for any ‘non-ignorable’ missingness
and methods to address missing data
` Methods to address nonlinearity and nonnormality
` Joint models adjusted for prognostic factors of performance status,
hi t l i l bt i h th t f di d
histological subtype, prior chemotherapy, stage of disease, gender, and age
` Use of latent class analysis to identify subpopulations of patients
` Need to confirm the findings in other cancer indications for
generalizability
` Decision-making should be informed by reliable patient self-reported assessment, in addition to clinically-p , y
reported symptoms
` Novel methods to analyze and interpret PRO/PFS data ` Allowed for better interpretation of PFS as a surrogate ` Allowed for better interpretation of PFS as a surrogate
endpoint to establish treatment benefit (Laffler, 2009) ` Directly informs comparative effectiveness research
P id t l d ti l f k f
` Provides a conceptual and operational framework for
integrated and methodological assessment of the clinical value of a medical treatment
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Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve
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Jassem J Ramlau R Santoro A Schuette W Chemaissani A Hong S et al (2008) Phase III trial Jassem, J., Ramlau, R., Santoro, A., Schuette, W., Chemaissani, A., Hong, S., et al. (2008). Phase III trial of pemetrexed plus best supportive care compared with best supportive care in previously treated
patients with advanced malignant pleural mesothelioma. Journal of Clinical Oncology, 26(10), 1698-1704. Laffler, M. J. (2009, September 16). FDA will revisit appropriate use of PFS endpoints at advisory
committee. The Pink Sheet.
Muthén, B., Asparouhov, T., Boye, M. E., Hackshaw, M. D., & Naegeli, A. N. (2009). Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report.
data using Mplus. Technical Report.
United States Food and Drug Administration [FDA]. (2007). Guidance for industry: Clinical trial endpoints for the approval of cancer drugs and biologics.
U it d St t F d d D Ad i i t ti [FDA] (2009) G id f i d t ti t t d United States Food and Drug Administration [FDA]. (2009). Guidance for industry on patient-reported outcome measures: Use in medical product development to support labeling claims.
